Data Transformation Trends

Gathered data is one thing. But useful data is quite another. Once data is collected, it has to be transformed in order to be of value to analytics and other platforms that glean insight from information.  

Digital Transformation 

Digital transformation has been a buzz word for a couple of years. But as companies lay the groundwork to actually digitally transform, the fruits of their endeavors are becoming evident. 

The McKinsey Changing Market Dynamics report notes that many organizations are lagging in their digital transformations. It stresses the value of creating a digital ecosystem that spans the entire supply chain. Those engaging in digital transformation, says the report, must commit to building an end-to-end platform. This requires definition of all functions and the extent of the value chain as well as collaboration on spare parts and consumables. Those engaged in such a journey, therefore, must avoid purchasing bit-and-piece data transformation and data management tools. They must implement it in such a way that each part contributes seamlessly to the whole. 

5 top data transformation trends

1. Data Cleansing

With so much data available, it is easy to get into the mindset that any datum is just as valuable as any other datum. Not so. Data much be thoroughly transformed for it to be truly useful. Worthless data needs to be stripped out of the data set, duplicates should be removed, and faulty data corrected. As data volumes have increased, the various processes required to boost the value of data have multiplied. 

“Data must be transformed in ways such as extracting, exporting, importing, cleaning it up, massaging it, aligning it, pre-processing, normalization, and compaction to be of real use by various applications,” said Greg Schulz, an analyst with StorageIO Group. 

2. Time to Value 

A study by Forrester showed that many organizations are facing challenges in harnessing data to develop valuable insights. In fact, 67% of businesses claim that they need more data than their current capabilities can provide, but 70% say that they are gathering data faster than they can analyze or use.  Either way, it is all about time to value. Whether the bottleneck is in collecting and transforming the data into a useable form, or in analyzing the data, businesses are grappling with how they can reduce the time it takes to transform their data into valuable insight. 

“Data overload, or the inability to extract actionable insights from data, is one of the top barriers preventing organizations from achieving their digital transformation goals,” said Darrel Ward, SVP of product management, Dell Technologies.

“Many factors contribute to this issue, but a lack of technology, data management processes , and a mature data culture with the skills to effectively handle the rapid influx of data are the most common reasons contributing to an organization’s data overload.”   

3. Shift to as-a-Service Transformation

An industry shift to as-a-service is part of the cure, added Ward. By providing more access to critical end-to-end technology and services, organizations will be able to develop the necessary processes that can help IT teams pull meaningful discoveries from their data, he said. 

Dell is among several companies addressing this issue. Dell Technologies’ Apex portfolio, for example, is an as-a-service that provides the choice to scale resources up or down to react to changes in the environment, while increasing control over the business. This approach enables companies to free up resources needed to manage infrastructure. It includes APEX Data Storage Services, APEX Hybrid and Private Cloud, and APEX Custom Solutions.

4. Application Sharing 

Data used to be processed or analyzed or otherwise used by only one system. These days, multiple systems want to gain access to it, add it into their own data, or use it to achieve some analytics, marketing, or business goal. But that poses a problem: Can the data be trusted, is it being viewed in the right context, and is it in a format that can be viewed and used by all relevant applications? Again, these problems are being addressed by the latest data management platforms. 

“Flexibly and securely viewing trusted data in context through shared applications across an industry ecosystem also enables process and governance improvement and optimal usage of human, asset, operational, and financial capital,” said Jeffrey Hojlo, an analyst at International Data Corp. (IDC).

5. Business First 

Old-school business intelligence and analytics platforms used to be built and then the business would figure out how to use them. This approach has been turned on its head. Success comes from having a clear business goal and use case and then aligning all technology elements to that vision. 

“Data transformation is about changing how data gets used, but everything must be tied to business goals and what the business expects from their ongoing digital transformation,” Schulz said.